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Communication Dans Un Congrès Année : 2012

A quasi-Newton proximal splitting method

Stephen Becker
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Jalal M. Fadili

Résumé

A new result in convex analysis on the calculation of proximity operators in certain scaled norms is derived. We describe efficient implementations of the proximity calculation for a useful class of functions; the implementations exploit the piece-wise linear nature of the dual problem. The second part of the paper applies the previous result to acceleration of convex minimization problems, and leads to an elegant quasi-Newton method. The optimization method compares favorably against state-of-the-art alternatives. The algorithm has extensive applications including signal processing, sparse recovery and machine learning and classification.
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Dates et versions

hal-01080081 , version 1 (04-11-2014)

Identifiants

  • HAL Id : hal-01080081 , version 1

Citer

Stephen Becker, Jalal M. Fadili. A quasi-Newton proximal splitting method. Neural Information Processing Systems (NIPS) 2012, Dec 2012, Lake Tahoe, Nevada, United States. pp.2618--2626. ⟨hal-01080081⟩
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